1. Field of the Invention
Embodiments of the present invention generally relate to contact centers and particularly to automatic optimization of performance within a contact center.
2. Description of Related Art
Contact centers are employed by many enterprises to service inbound and outbound contacts from customers. A typical contact center includes a switch and/or server to receive and route incoming packet-switched and/or circuit-switched contacts and one or more resources, such as human agents and automated resources (e.g., Interactive Voice Response (IVR) units), to service the incoming contacts (i.e., customers). Contact centers distribute contacts, whether inbound or outbound, for providing service to any suitable resource according to predefined criteria. In many existing systems, the criteria for serving the contact from the moment that the contact center becomes aware of the contact until the contact is connected to an agent are client or operator-specifiable (i.e., programmable by the operator of the contact center), via a capability called vectoring. Normally in present-day Automatic Call distributors (or ACDs) when the ACD system's controller detects that an agent has become available to handle a contact, the controller identifies all predefined contact-handling queues for the agent, usually in some order of priority and delivers to the agent the highest-priority, oldest contact that matches the agent's highest-priority queue.
The primary objective of contact center management is to ultimately maximize contact center performance and profitability. That may involve minimizing cost, maximizing contact throughput, and/or maximizing revenue. Further, ongoing challenges in contact center administration involve optimizing contact center efficiency and keeping the contacts satisfied from the service offered by the contact center.
Contact center efficiency is generally measured in two ways, i.e., service level and match rate. Service level is typically determined by dividing the number of contacts accepted within the specified period by the number accepted plus the number that were not accepted, but completed in some other way (e.g., abandoned, given busy, canceled, flowed out). Of course, service level definitions may vary from one enterprise to another.
Further, match rate is usually determined by dividing the number of contacts accepted by a primary skill level agent within a period of time by the number of contacts accepted by any agent for a queue over the same period. An agent with a primary skill level is one that typically can handle contacts of a certain nature most effectively and/or efficiently. There are other contact center agents that may not be as proficient as the primary skill level agent, and those agents are identified either as secondary skill level agents or backup skill level agents. As can be appreciated, contacts received by a primary skill level agent are typically handled more quickly and accurately or effectively (e.g., higher revenue attained) than a contact received by a secondary or even backup skill level agent. Thus, it is an objective of most contact centers to optimize match rate along with service level.
In addition to service level and match rate performance measures, contact centers use other Key Performance Indicators (“KPIs”), such as revenue, estimated, actual, or predicted wait time, average speed of answer, throughput, agent utilization, agent performance, agent responsiveness and the like, to calculate performance relative to their Service Level Agreements (“SLAs”).
Throughput of a contact center is a measure of the number of calls/contact requests or work requests that can be processed by an agent in a given amount of time. Further, to enhance the throughput, agents' utilization and customer satisfaction are the main factors of the contact center. Agent utilization is a measure of how efficiently agents' time is being used. Customer service level is a measure of the time customers spend waiting for their work to be handled. Company contact center wishes to provide service to as many requests as possible in a given amount of time by minimizing the wait time for their customers.
Typically, supervisors in a contact center manage KPIs such as wait time for each queue that may have one or more agents. If a KPI is about to exceed an agreed SLA, supervisors modify parameters such as skills of agents or group of agents in order to increase the number of agents associated with a specific queue requiring one or more specific skills. Further, supervisors can change the number of agents associated with a specific region. Alternatively, supervisors may change the percentage of calls assigned to be handled by a specific queue or a pool of agents. For example, if customer wait times increase or KPIs show low performance of an agent or a group of agents then a supervisor may direct 15% of calls rather than 20% of calls to that agent or the group of agents or a specific outsourcer.
As a result of such parameter modification, KPI of a specific queue may improve. However, KPI of other queues may degrade since fewer agents are assigned to handle callers in that queue. For example, in case, a specific queue has a low KPI due to less skilled agent(s) associated with that queue, then a supervisor may add another agent to that queue from some other queue, and accordingly, the KPI of the other queue may degrade due to reduction in the number of skilled agents from the other queue. In such case, further trial and error method may be used by the supervisors to maintain performance of each queue and so on. Thus, the overall performance of the contact center may not be retained through such manual, trial and error based method. Also, existing solutions may consume significant amount of time on supervisor's behalf to decide for agents' shifting from one queue or department to another queue or department in the effort of maintaining the performance of the contact center.
Based on the aforementioned shortcomings of the background art, systems and a method are needed to overcome existing challenges and to improve the performance of the contact center and in turn further improve agent utilization and customer satisfaction. Thus, the contact center should be able to implement time-effective and automated methods for enhancing overall performance of the contact center.